國立虎尾科技大學 |

Compatibility modeling : = data and knowledge applications for clothing matching /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Compatibility modeling :/ Xuemeng Song, Liqiang Nie, Yinglong Wang.
Reminder of title:	data and knowledge applications for clothing matching /
remainder title:	Data and knowledge applications for clothing matching.
Author:	Song, Xuemeng
other author:	Nie, Liqiang,
Description:	1 PDF (xix, 118 pages) :color illustrations. :
Notes:	Part of: Synthesis digital library of engineering and computer science.
Subject:	Clothing and dress - Data processing. -
Online resource:	https://ieeexplore.ieee.org/servlet/opac?bknumber=8890788
Online resource:	https://doi.org/10.2200/S00952ED1V01Y201909ICR069
ISBN:	9781681736693

Compatibility modeling : = data and knowledge applications for clothing matching /
Song, Xuemeng(Computer scientist),

Compatibility modeling :data and knowledge applications for clothing matching /Data and knowledge applications for clothing matching.Xuemeng Song, Liqiang Nie, Yinglong Wang. - 1 PDF (xix, 118 pages) :color illustrations. - Synthesis lectures on information concepts, retrieval, and services ;#691947-9468 ;. - Synthesis digital library of engineering and computer science..

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 103-116).

1. Introduction -- 1.1. Background -- 1.2. Challenges -- 1.3. Our solutions -- 1.4. Book structure

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Compendex

Nowadays, fashion has become an essential aspect of people's daily life. As each outfit usually comprises several complementary items, such as a top, bottom, shoes, and accessories, a proper outfit largely relies on the harmonious matching of these items. Nevertheless, not everyone is good at outfit composition, especially those who have a poor fashion aesthetic. Fortunately, in recent years the number of online fashion-oriented communities, like IQON and Chictopia, as well as e-commerce sites, like Amazon and eBay, has grown. The tremendous amount of real-world data regarding people's various fashion behaviors has opened a door to automatic clothing matching. Despite its significant value, compatibility modeling for clothing matching that assesses the compatibility score for a given set of (equal or more than two) fashion items, e.g., a blouse and a skirt, yields tough challenges: (a) the absence of comprehensive benchmark; (b) comprehensive compatibility modeling with the multi-modal feature variables is largely untapped; (c) how to utilize the domain knowledge to guide the machine learning; (d) how to enhance the interpretability of the compatibility modeling; and (e) how to model the user factor in the personalized compatibility modeling. These challenges have been largely unexplored to date. In this book, we shed light on several state-of-the-art theories on compatibility modeling. In particular, to facilitate the research, we first build three large-scale benchmark datasets from different online fashion websites, including IQON and Amazon. We then introduce a general data-driven compatibility modeling scheme based on advanced neural networks. To make use of the abundant fashion domain knowledge, i.e., clothing matching rules, we next present a novel knowledge-guided compatibility modeling framework. Thereafter, to enhance the model interpretability, we put forward a prototype-wise interpretable compatibility modeling approach. Following that, noticing the subjective aesthetics of users, we extend the general compatibility modeling to the personalized version. Moreover, we further study the real-world problem of personalized capsule wardrobe creation, aiming to generate a minimum collection of garments that is both compatible and suitable for the user. Finally, we conclude the book and present future research directions, such as the generative compatibility modeling, virtual try-on with arbitrary poses, and clothing generation.

Mode of access: World Wide Web.

ISBN: 9781681736693

Standard No.: 10.2200/S00952ED1V01Y201909ICR069doiSubjects--Topical Terms:

1253147
Clothing and dress
--Data processing.Subjects--Index Terms:

compatibility modelingIndex Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: TT507 / .S663 2020eb

Dewey Class. No.: 746.92

Compatibility modeling : = data and knowledge applications for clothing matching /
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245 1 0 $a Compatibility modeling : $b data and knowledge applications for clothing matching / $c Xuemeng Song, Liqiang Nie, Yinglong Wang.
246 3 0 $a Data and knowledge applications for clothing matching.
264 1 $a [San Rafael, California] : $b Morgan & Claypool, $c [2020]
300 $a 1 PDF (xix, 118 pages) : $b color illustrations.
336 $a text $2 rdacontent
337 $a electronic $2 isbdmedia
338 $a online resource $2 rdacarrier
490 1 $a Synthesis lectures on information concepts, retrieval, and services ; $x 1947-9468 ; $v #69
500 $a Part of: Synthesis digital library of engineering and computer science.
504 $a Includes bibliographical references (pages 103-116).
505 0 $a 1. Introduction -- 1.1. Background -- 1.2. Challenges -- 1.3. Our solutions -- 1.4. Book structure
505 8 $a 2. Data collection -- 2.1. Dataset I for general compatibility modeling -- 2.2. Dataset II for personalized compatibility modeling -- 2.3. Dataset III for personalized wardrobe creation -- 2.4. Summary
505 8 $a 3. Data-driven compatibility modeling -- 3.1. Introduction -- 3.2. Related work -- 3.3. Methodology -- 3.4. Experiment -- 3.5. Summary
505 8 $a 4. Knowledge-guided compatibility modeling -- 4.1. Introduction -- 4.2. Related work -- 4.3. Methodology -- 4.4. Experiment -- 4.5. Summary
505 8 $a 5. Prototype-wise interpretable compatibility modeling -- 5.1. Introduction -- 5.2. Related work -- 5.3. Methodology -- 5.4. Experiment -- 5.5. Summary
505 8 $a 6. Personalized compatibility modeling -- 6.1. Introduction -- 6.2. Related work -- 6.3. Methodology -- 6.4. Experiment -- 6.5. Summary
505 8 $a 7. Personalized capsule wardrobe creation -- 7.1. Introduction -- 7.2. Related work -- 7.3. PCW-DC -- 7.4. Body shape assignment scheme -- 7.5. Experiments -- 7.6. Summary
505 8 $a 8. Research frontiers -- 8.1. Generative compatibility modeling -- 8.2. Virtual try-on with arbitrary pose -- 8.3. Clothing generation.
506 $a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 $a Compendex
510 0 $a INSPEC
510 0 $a Google scholar
510 0 $a Google book search
520 $a Nowadays, fashion has become an essential aspect of people's daily life. As each outfit usually comprises several complementary items, such as a top, bottom, shoes, and accessories, a proper outfit largely relies on the harmonious matching of these items. Nevertheless, not everyone is good at outfit composition, especially those who have a poor fashion aesthetic. Fortunately, in recent years the number of online fashion-oriented communities, like IQON and Chictopia, as well as e-commerce sites, like Amazon and eBay, has grown. The tremendous amount of real-world data regarding people's various fashion behaviors has opened a door to automatic clothing matching. Despite its significant value, compatibility modeling for clothing matching that assesses the compatibility score for a given set of (equal or more than two) fashion items, e.g., a blouse and a skirt, yields tough challenges: (a) the absence of comprehensive benchmark; (b) comprehensive compatibility modeling with the multi-modal feature variables is largely untapped; (c) how to utilize the domain knowledge to guide the machine learning; (d) how to enhance the interpretability of the compatibility modeling; and (e) how to model the user factor in the personalized compatibility modeling. These challenges have been largely unexplored to date. In this book, we shed light on several state-of-the-art theories on compatibility modeling. In particular, to facilitate the research, we first build three large-scale benchmark datasets from different online fashion websites, including IQON and Amazon. We then introduce a general data-driven compatibility modeling scheme based on advanced neural networks. To make use of the abundant fashion domain knowledge, i.e., clothing matching rules, we next present a novel knowledge-guided compatibility modeling framework. Thereafter, to enhance the model interpretability, we put forward a prototype-wise interpretable compatibility modeling approach. Following that, noticing the subjective aesthetics of users, we extend the general compatibility modeling to the personalized version. Moreover, we further study the real-world problem of personalized capsule wardrobe creation, aiming to generate a minimum collection of garments that is both compatible and suitable for the user. Finally, we conclude the book and present future research directions, such as the generative compatibility modeling, virtual try-on with arbitrary poses, and clothing generation.
530 $a Also available in print.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Title from PDF title page (viewed on November 27, 2019).
650 0 $a Clothing and dress $x Data processing. $3 1253147
650 0 $a Machine learning. $3 561253
650 0 $a Decision making. $3 528319
653 $a compatibility modeling
653 $a clothing matching
653 $a interpretable modeling
653 $a knowledge distillation
653 $a preference modeling
653 $a personalized capsule wardrobe
655 0 $a Electronic books. $2 local $3 554714
700 1 $a Nie, Liqiang, $e author. $3 1253145
700 1 $a Wang, Yinglong $c (Researcher), $e author. $3 1253146
776 0 8 $i Print version: $z 9781681736686 $z 9781681736709
830 0 $a Synthesis digital library of engineering and computer science. $3 598254
830 0 $a Synthesis lectures on information concepts, retrieval, and services ; $v #22. $3 866817
856 4 2 $3 Abstract with links to resource $u https://ieeexplore.ieee.org/servlet/opac?bknumber=8890788
856 4 0 $3 Abstract with links to full text $u https://doi.org/10.2200/S00952ED1V01Y201909ICR069